Discovering Pair-Wise Genetic Interactions: An Information Theory-Based Approach

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.

表型变异（Phenotypic variation）的产生部分源于遗传变异与环境因素间的多重交互作用，其中亦涵盖构成人类健康与疾病基础的表型变异。尽管由单基因变异（single gene variants）引发的疾病或表型，可通过成熟的关联分析法与家系研究法进行鉴定，但由多基因交互作用（multi-gene interactions）导致的复杂表型性状，仍极难实现精准解析。本研究提出一种基于信息论（information theory）的全新方法，并阐释了该方法如何在既往研究方法的基础上，优化遗传交互作用的鉴定流程，涵盖合成型与修饰型两类遗传交互作用。我们将该方法的量化指标——交互距离（interaction distance）——应用于此前已分析过的酵母孢子形成效率数据集、小鼠脂质相关数据集以及多个人类疾病模型数据集，以完成对该方法的性能表征与验证。我们证实，交互距离可在实验数据集与模拟数据集中发掘全新的基因交互作用候选靶点，且在多种场景下的性能优于其他同类量化指标。该方法还可助力优化临床研究中的病例-对照样本配比方案。